Tamer integration for unified-planning

Tamer is an application-oriented planner for the ANML planning specification language. The objective of Tamer is to provide functionalities to model, solve and analyze planning problems in practice, with advanced temporal features.

Tamer engine

The Tamer engine supports action-based planning, with the following features:

• classical: basic action models with symbolic state variables
• numeric: support numeric planning over integer and real state variables.
• temporal: durative actions, intermediate conditions and effects, timed initial literals, timed goals. Support continuous time semantics.

Provided engines:

• tamer:
  • oneshot planning: Will return the first plan found, regardless of its quality.
  • plan validation: Will analyze a plan and return if the plan is valid or not.

Default configuration

The Tamer integration for unified-planning uses an heuristic search algorithm to solve the planning problem. More specifically, the default search is a Weighted A* Search, with weight equals to 0.8 and hadd as heuristic.

The custom parameters are:

• weight: a float between 0.0 and 1.0,
• heuristic: a string between hadd, hlandmarks, hmax, hff and blind.

Installation

To automatically get a version that works with your version of the unified planning framework, you can list it as a solver in the pip installation of unified_planning:

pip install unified-planning[tamer]

If you need several solvers, you can list them all within the brackets.

You can also install the Tamer integration separately (in case the current version of unified_planning does not include Tamer or you want to add it later to your unified planning installation). With

pip install up-tamer

you get the latest version. If you need an older version, you can install it with:

pip install up-tamer==<version number>

If you need the latest pre-release version, you can install it with:

pip install --pre up-tamer

or if you already have a version installed:

pip install --pre --upgrade up-tamer

References

• Alessandro Valentini, Andrea Micheli and Alessandro Cimatti. Temporal Planning with Intermediate Conditions and Effects. In AAAI 2020.

Acknowledgments

This library is being developed for the AIPlan4EU H2020 project (https://aiplan4eu-project.eu) that is funded by the European Commission under grant agreement number 101016442.